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Register a new userIntelligent Reflecting Surface Aided Secure UAV Communications
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Added by
Wen Wang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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In this letter, we study the secure communication problem in the unmanned aerial vehicle (UAV) enabled networks aided by an intelligent reflecting surface (IRS) from the physical-layer security perspective. Specifically, the IRS is deployed to assist the wireless transmission from the UAV to the ground user in the presence of an eavesdropper. The objective of this work is to maximize the secrecy rate by jointly optimizing the phase shifts at the IRS as well as the transmit power and location of the UAV. However, the formulated problem is difficult to solve directly due to the non-linear and non-convex objective function and constraints. By invoking fractional programming and successive convex approximation techniques, the original problem is decomposed into three subproblems, which are then transformed into convex ones. Next, a low-complexity alternating algorithm is proposed to solve the challenging non-convex problem effectively, where the closed-form expressions for transmit power and phase shifts are obtained at each iteration. Simulations results demonstrate that the designed algorithm for IRS-aided UAV communications can achieve higher secrecy rate than benchmarks.
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We introduce a novel system setup where a backscatter device operates in the presence of an intelligent reflecting surface (IRS). In particular, we study the bistatic backscatter communication (BackCom) system assisted by an IRS. The phase shifts at the IRS are optimized jointly with the transmit beamforming vector of the carrier emitter to minimize the transmit power consumption at the carrier emitter whilst guaranteeing a required BackCom performance. The unique channel characteristics arising from multiple reflections at the IRS render the optimization problem highly non-convex. Therefore, we jointly utilize the minorization-maximization algorithm and the semidefinite relaxation technique to present an approximate solution for the optimal IRS phase shift design. We also extend our analytical results to the monostatic BackCom system. Numerical results indicate that the introduction of the IRS brings about considerable reductions in transmit power, even with moderate IRS sizes, which can be translated to range increases over the non-IRS-assisted BackCom system.
In a practical massive MIMO (multiple-input multiple-output) system, the number of antennas at a base station (BS) is constrained by the space and cost factors, which limits the throughput gain promised by theoretical analysis. This paper thus studies the feasibility of adopting the intelligent reflecting surface (IRS) to further improve the beamforming gain of the uplink communications in a massive MIMO system. Under such a novel system, the central question lies in whether the IRS is able to enhance the network throughput as expected, if the channel estimation overhead is taken into account. In this paper, we first show that the favorable propagation property for the conventional massive MIMO system without IRS, i.e., the channels of arbitrary two users are orthogonal, no longer holds for the IRS-assisted massive MIMO system, due to its special channel property that each IRS element reflects the signals from all the users to the BS via the same channel. As a result, the maximal-ratio combining (MRC) receive beamforming strategy leads to strong inter-user interference and thus even lower user rates than those of the massive MIMO system without IRS. To tackle this challenge, we propose a novel strategy for zero-forcing (ZF) beamforming design at the BS and reflection coefficients design at the IRS to efficiently null the inter-user interference. Under our proposed strategy, it is rigorously shown that even if the channel estimation overhead is considered, the IRS-assisted massive MIMO system can always achieve higher throughput compared to its counterpart without IRS, despite the fact that the favorable propagation property no longer holds.
In this paper, the adoption of an intelligent reflecting surface (IRS) for multiple single-antenna source terminal (ST)-DT pairs in two-hop networks is investigated. Different from the previous studies on IRS that merely focused on tuning the reflection coefficient of all the reflection elements at IRS, in this paper, we consider the true reflection resource management. Specifically, the true reflection resource management can be realized via trigger module selection based on our proposed IRS architecture that all the reflection elements are partially controlled by multiple parallel switches of controller. As the number of reflection elements increases, the true reflection resource management will become urgently needed in this context, which is due to the non-ignorable energy consumption. Moreover, the proposed modular architecture of IRS is designed to make the reflection elements part independent and controllable. As such, our goal is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) at DTs via a joint trigger module subset selection, transmit power allocation of STs, and the corresponding passive beamforming of the trigger modules, subject to per ST power budgets and module size constraint. Whereas this problem is NP-hard due to the module size constraint, to deal with it, we transform the hard module size constraint into the group sparse constraint by introducing the mixed row block norm, which yields a suitable semidefinite relaxation. Additionally, the parallel alternating direction method of multipliers (PADMM) is proposed to identify the trigger module subset, and then subsequently the transmit power allocation and passive beamforming can be obtained by solving the original minimum SINR maximization problem without the group sparse constraint via partial linearization for generalized fractional programs.
Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new transmission framework is proposed, where multiple UAV-mounted base stations employ NOMA to serve multiple groups of ground users with the aid of an IRS. The three-dimensional (3D) placement and transmit power of UAVs, the reflection matrix of the IRS, and the NOMA decoding orders among users are jointly optimized for maximization of the sum rate of considered networks. To tackle the formulated mixed-integer non-convex optimization problem with coupled variables, a block coordinate descent (BCD)-based iterative algorithm is developed. Specifically, the original problem is decomposed into three subproblems, which are alternatingly solved by exploiting the penalty method and the successive convex approximation technique. The proposed BCD-based algorithm is demonstrated to be able to obtain a stationary point of the original problem with polynomial time complexity. Numerical results show that: 1) the proposed NOMA-IRS scheme for multi-UAV networks achieves a higher sum rate compared to the benchmark schemes, i.e., orthogonal multiple access (OMA)-IRS and NOMA without IRS; 2) the use of IRS is capable of providing performance gain for multi-UAV networks by both enhancing channel qualities of UAVs to their served users and mitigating the inter-UAV interference; and 3) optimizing the UAV placement can make the sum rate gain brought by NOMA more distinct due to the flexible decoding order design.
Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to provide communication service for remote mobile users due to their high mobility and flexible deployment. However, the line-of-sight (LoS) wireless links are vulnerable to be intercepted by the eavesdropper (Eve), which presents a major challenge for UAV-aided communications. In this paper, we propose a latency-minimized transmission scheme for satisfying legitimate users (LUs) content requests securely against Eve. By leveraging physical-layer security (PLS) techniques, we formulate a transmission latency minimization problem by jointly optimizing the UAV trajectory and user association. The resulting problem is a mixed-integer nonlinear program (MINLP), which is known to be NP hard. Furthermore, the dimension of optimization variables is indeterminate, which again makes our problem very challenging. To efficiently address this, we utilize bisection to search for the minimum transmission delay and introduce a variational penalty method to address the associated subproblem via an inexact block coordinate descent approach. Moreover, we present a characterization for the optimal solution. Simulation results are provided to demonstrate the superior performance of the proposed design.
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